An Opinion-Leader Mining Method in Social Networks With a Phased-Clustering Perspective

Zhang, Bo; Bai, Yanhong; Zhang, Qian; Lian, Jie; Li, Meizi

doi:10.1109/access.2020.2972997

Cited by 20 publications

(8 citation statements)

References 36 publications

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“…This complex propagation phenomenon exists in many areas, such as business and marketing [ 6 ], politics [ 7 ], public health [ 8 ], education [ 9 ], and social [ 10 ], among others. Owing to social influence and homogeneity [ 11 ], the opinion leaders’ characteristics of novelty, activity, and professional knowledge influence both the behavior of the other members of the community and decision making [ 12 ]. Opinion leaders play a key role in community management, public opinion guidance, and other decisions.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Robustness of Open-Source Project Knowledge Collaborative Network Based on Opinion Leader Identification

Lei

Zhang

Liu

2021

Entropy

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A large amount of semantic content is generated during designer collaboration in open-source projects (OSPs). Based on the characteristics of knowledge collaboration behavior in OSPs, we constructed a directed, weighted, semantic-based knowledge collaborative network. Four social network analysis indexes were created to identify the key opinion leader nodes in the network using the entropy weight and TOPSIS method. Further, three degradation modes were designed for (1) the collaborative behavior of opinion leaders, (2) main knowledge dissemination behavior, and (3) main knowledge contribution behavior. Regarding the degradation model of the collaborative behavior of opinion leaders, we considered the propagation characteristics of opinion leaders to other nodes, and we created a susceptible–infected–removed (SIR) propagation model of the influence of opinion leaders’ behaviors. Finally, based on empirical data from the Local Motors open-source vehicle design community, a dynamic robustness analysis experiment was carried out. The results showed that the robustness of our constructed network varied for different degradation modes: the degradation of the opinion leaders’ collaborative behavior had the lowest robustness; this was followed by the main knowledge dissemination behavior and the main knowledge contribution behavior; the degradation of random behavior had the highest robustness. Our method revealed the influence of the degradation of collaborative behavior of different types of nodes on the robustness of the network. This could be used to formulate the management strategy of the open-source design community, thus promoting the stable development of OSPs.

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Section: Introductionmentioning

confidence: 99%

Dynamic Robustness of Open-Source Project Knowledge Collaborative Network Based on Opinion Leader Identification

Lei

Zhang

Liu

2021

Entropy

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“…The following study proposes a similar algorithm baselining PageRank algorithm by utilizing the K-means algorithm. In a study conducted by Zhang et al (2020), a rank after clustering (RaCRank) algorithm is proposed to detect opinion leaders in social networks [42]. The algorithm consists of two phases.…”

Section: Speed Of Getting a Reactionmentioning

confidence: 99%

“…Although the RaCRank algorithm performs slightly worse than UI_LR, it outperforms AllClusterRank. According to [41,42], future studies can focus on detection of topic-based opinion leaders.…”

Section: Speed Of Getting a Reactionmentioning

confidence: 99%

From past to present: Spam detection and identifying opinion leaders in social networks

Altınel

Gumuscekicci

2022

Sigma J Eng Nat Sci - Sigma Müh Fen Bil Derg

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On microblogging sites, which are gaining more and more users every day, a wide range of ideas are quickly emerging, spreading, and creating interactive environments. In some cases, in Turkey as well as in the rest of the world, it was noticed that events were published on microblogging sites before appearing in visual, audio and printed news sources. Thanks to the rapid flow of information in social networks, it can reach millions of people in seconds. In this context, social media can be seen as one of the most important sources of information affecting public opinion. Since the information in social networks became accessible, research started to be conducted using the information on the social networks. While the studies about spam detection and identification of opinion leaders gained popularity, surveys about these topics began to be published. This study also shows the importance of spam detection and identification of opinion leaders in social networks. It is seen that the data collected from social platforms, especially in recent years, has sourced many state-of-art applications. There are independent surveys that focus on filtering the spam content and detecting influencers on social networks. This survey analyzes both spam detection studies and opinion leader identification and categorizes these studies by their methodologies. As far as we know there is no survey that contains approaches for both spam detection and opinion leader identification in social networks. This survey contains an overview of the past and recent advances in both spam detection and opinion leader identification studies in social networks. Furthermore, readers of this survey have the opportunity of understanding general aspects of different studies about spam detection and opinion leader identification while observing key points and comparisons of these studies.

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“…Opinion mining can deal with a large number of documents in the form of text or scores [30], [31]. Opinion mining has been applied in a wide range of fields [32]- [33] [34]. The general process for conducting opinion mining research consists of three phases.…”

Section: A Opinion Miningmentioning

confidence: 99%

Detecting a Risk Signal in Stock Investment Through Opinion Mining and Graph-Based Semi-Supervised Learning

2020

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The objective of this study is to develop an algorithm to support a decision-making process in stock investment through opinion mining and graph-based semi-supervised learning. For this purpose, this research addresses the following core processes: (1) filtering fake information, (2) assessing credit risk and detecting risk signals, and (3) predicting future occurrences of credit events through sentiment analysis, word2vec, and graph-based semi-supervised learning. First, financial data, including news, texts in social network services, and financial statements, were collected. Among these data, fake information such as rumors and fake news was filtered by author analysis and a rule-based approach. Second, credit risk was assessed by opinion mining and sentiment analysis for both social data and news in the form of a sentiment score and the trend of documents for each stock. A signal for a credit event was then detected by the degree of assessed risk. Consequently, the possibility of credit events such as delisting and bankruptcy in the near future was forecast based on the risk signal using logistic regression. This research illustrated the real case of a company to validate the applicability of the proposed approach. The results of this study can help investors monitor a large amount of historically accumulated data and detect hidden signals of risk events ahead of time.

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An Opinion-Leader Mining Method in Social Networks With a Phased-Clustering Perspective

Cited by 20 publications

References 36 publications

Dynamic Robustness of Open-Source Project Knowledge Collaborative Network Based on Opinion Leader Identification

Dynamic Robustness of Open-Source Project Knowledge Collaborative Network Based on Opinion Leader Identification

From past to present: Spam detection and identifying opinion leaders in social networks

Detecting a Risk Signal in Stock Investment Through Opinion Mining and Graph-Based Semi-Supervised Learning

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